Category: Misc

Today, in partnership with NVIDIA, Google Cloud announced Dataflow is bringing GPUs to the world of big data processing to unlock new possibilities. With Dataflow GPU, users can now leverage the power of NVIDIA GPUs in their machine learning inference workflows. Here we show you how to access these performance benefits with BERT.  Google Cloud’s Dataflow … Continued

Today, in partnership with NVIDIA, Google Cloud announced Dataflow is bringing GPUs to the world of big data processing to unlock new possibilities. With Dataflow GPU, users can now leverage the power of NVIDIA GPUs in their machine learning inference workflows. Here we show you how to access these performance benefits with BERT. 

Google Cloud’s Dataflow is a managed service for executing a wide variety of data processing patterns including both streaming and batch analytics. It has recently added GPU support can now accelerate machine learning inference workflows, which are running on Dataflow pipelines. 

Please check out Google Cloud’s launch post for more exciting new features. In this post, we will showcase the performance benefits and TCO improvement with NVIDIA GPU acceleration by deploying a Bidirectional Encoder Representations from Transformers (BERT) model fine-tuned on “Question Answering” tasks on Dataflow. We show TensorFlow inference in Dataflow with CPU, how to run the same code on GPU with a significant performance boost, showcase the best performance after we convert the model through NVIDIA TensorRT, and deploy through TensorRT’s python API with Dataflow. Check out NVIDIA sample code to try now. 

There are several steps we will be touching on in this post. We start by creating an environment on our local machine to run all of these Dataflow jobs. For additional details, please refer to the Dataflow Python quick start guide.

Creating an environment

 It is recommended to create a virtual environment for Python, we use virtualenv here:

virtualenv -p 

When using Dataflow, it is required to align the Python version in your development environment with the Dataflow runtime Python version. More specifically, when running a Dataflow pipeline, you should use the same Python version and Apache Beam SDK version to avoid unexpected errors.

Now, we activate the virtual environment.

source /bin/activate

One of the most important things to pay attention to before activating a virtual environment is to be sure that you are not operating in another virtual environment, as this usually causes issues.

After activating our virtual environment, we are ready to install the required packages. Even though our jobs are running on Dataflow, we still need a couple of packages locally so that Python does not complain when we run our code locally.

pip install apache-beam[gcp]
pip install TensorFlow==2.3.1

You can experiment with different versions of TensorFlow but the key here is to align the version you have here and the version that you will be using in the Dataflow environment. Apache Beam and its Google Cloud components are also required.

Getting the fine-tuned BERT model

NVIDIA NGC has plenty of resources ranging from GPU-optimized containers to fine-tuned models. We explore several NGC resources.

The first resource we will be using is a BERT large model that is fine-tuned for the SquadV2 question answering task and contains 340 million parameters. The following command will download the BERT model.

wget --content-disposition 
https://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_savedmodel_large_qa_squad2_amp_384/versions/19.03.0/zip -O bert_tf_savedmodel_large_qa_squad2_amp_384_19.03.0.zip

With the BERT model we just downloaded, automatic mixed precision (AMP) is used during training and the sequence length is 384.

We also need a vocabulary file and we get it from a BERT checkpoint that can be obtained from NGC with the following command:

wget --content-disposition 
https://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_ckpt_large_qa_squad2_amp_128/versions/19.03.1/zip -O bert_tf_ckpt_large_qa_squad2_amp_128_19.03.1.zip

After getting these resources, we just need to uncompress them and locate them in our working folder. We will be using a custom docker container and these models will be included in our image.

Custom Dockerfile

We will be using a custom Dockerfile that is derived from a GPU-optimized NGC TensorFlow container. NGC TensorFlow (TF) containers are the best option when accelerating TF models using NVIDIA GPUs.

We then add a couple of more steps to copy these models and the files we have. You can find the Dockerfile here and below is a snapshot of the Dockerfile.

FROM nvcr.io/nvidia/tensorflow:20.11-tf2-py3
RUN pip install --no-cache-dir apache-beam[gcp]==2.26.0 ipython pytest pandas && 
    mkdir -p /workspace/tf_beam
COPY --from=apache/beam_python3.6_sdk:2.26.0 /opt/apache/beam /opt/apache/beam
ADD. /workspace/tf_beam
WORKDIR /workspace/tf_beam
ENTRYPOINT [ "/opt/apache/beam/boot"]

The next steps are to build the docker file and push it to the Google Container Registry (GCR). You can do this with the following command. Alternatively, you can use the script we created here. If you are using the script from our repo, you can simply do bash build_and_push.sh

project_id=""
docker build . -t "gcr.io/${project_id}/tf-dataflow-${USER}:latest"
docker push "gcr.io/${project_id}/tf-dataflow-${USER}:latest"

Running jobs

If you have already authenticated your Google account, you can simply run the Python files we provided here by calling the run_cpu.sh and run_gpu.sh scripts are available in the same repo.

CPU TensorFlow Inference in Dataflow (TF-CPU)

The bert_squad2_qa_cpu.py file in the repo is designed to answer questions based on a description text document. The batch size is 16, meaning that we will be answering 16 questions at each inference call and there are 16,000 questions (1,000 batches of questions). Note that BERT could be fine-tuned for other tasks given a specific use case.

When running a job on Dataflow, by default it auto-scales based on real-time CPU usage. If you want to disable this feature you need to set autoscaling_algorithm to NONE. This will let you pick how many workers to use throughout the life of your job. Alternatively, you can let Dataflow auto-scale your job and limit the maximum number of workers to be used by setting the max_num_workers parameter.

We recommend setting a job name rather than using the auto-generated name to better follow your jobs by setting the job_name parameter. This job name will be the prefix for the compute instance that is running your job.

Accelerating with GPU (TF-GPU)

To execute the same dataflow TensorFlow inference job with GPU support, we need to set the following parameters. For additional information, please refer to Dataflow GPU documentation. For additional information, please refer to Dataflow GPU documentation.

--experiment "worker_accelerator=type:nvidia-tesla-t4;count:1;install-nvidia-driver"

The parameter preceding enables us to have an NVIDIA T4 Tensor Core GPU attached to the Dataflow worker VM, which is also visible as a Compute VM instance running our job. Dataflow will automatically install required NVIDIA drivers that support CUDA11.

The bert_squad2_qa_gpu.py file is almost the same as the bert_squad2_qa_cpu.py file. This means that with very little to no changes we can have our jobs running using NVIDIA GPUs. In our examples, we have a couple of additional GPU setups such as setting the memory growth with the code below.

physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

Inference with NVIDIA optimized libraries

NVIDIA TensorRT optimizes Deep Learning models for inference and provides low latency and high throughput (for more information). Here, we use the NVIDIA TensorRT optimization to BERT model and use it to answer questions on a Dataflow pipeline with GPU at the speed of light. Users could follow the TensorRT demo BERT github repository.

We also use Polygraphy, which is a high-level python API for TensorRT to load the TensorRT engine file and run inference. In Dataflow code, the TensorRT model is encapsulated with a shared utility class, allowing all threads from a Dataflow worker process to make use of it.

Comparing CPU and GPU runs

In Table 10, we provided total run times and resources used for sample runs. The final cost for a Dataflow job is a linear combination of total vCPU time, total memory time, and total hard disk usage. For the GPU case, there is a GPU component as well.

Note that the table preceding is compiled based on a run and the exact number might slightly fluctuate but according to our experiments the ratios did not change much.

The total savings including the cost and run-time savings is more than 10x when accelerating our model with NVIDIA GPUs (TF-GPU) compared to using CPUs (TF-CPU). This means that when we use NVIDIA GPUs for inference on this task, we can have faster run times and lower costs compared to running your model using only CPUs.

With NVIDIA optimized inference libraries such as TensorRT, the user could run more complex and bigger models on GPU in Dataflow. TensorRT further accelerates the same job 3.6x faster compared to running it with TF-GPU, which yields 4.2x cost saving. Compare TensorRT with TF-CPU, we get 17x less execution time that provides around 38x less bill.

Summary

In this post, we compared TF-CPU, TF-GPU, and TensorRT inference performance for the question answering task running on Google Cloud Dataflow. Dataflow users can get great benefits by leveraging GPU workers and NVIDIA optimized libraries.

Accelerating deep learning model inference with NVIDIA GPUs and NVIDIA software is super easy. By adding or changing a couple of lines, we can run models using TF-GPU or TensorRT. We provided scripts and source files here and here for reference.

Acknowledgments

We would like to thank Shan Kulandaivel, Valentyn Tymofieiev, and Reza Rokni from the Google Cloud Dataflow team, and Jill Milton and Fraser Gardiner from NVIDIA for their support and invaluable feedback.

We’ve been counting down to the release of Ray Tracing Gems II by providing early releases of select chapters once every week in July. This week’s chapter presents two real-time techniques for rendering caustics effects with ray tracing.

In just two weeks, on August 4, Ray Tracing Gems II will be available to download for free in its entirety, or to purchase as a physical release from Apress or Amazon. We’ve been counting down to this date by providing early releases of select chapters once every week in July. Today’s chapter presents two real-time techniques for rendering caustics effects with ray tracing. The first is built on an adaptive photon scattering approach that can depict accurate caustic patterns from metallic and transparent surfaces after multiple ray bounces. The second is specialized for water caustics cast after a single-bounce reflection or refraction and is appropriate for use on water surfaces that cover large areas of a scene. Both techniques are fully dynamic, low cost, and ready-to-use with no data preprocessing requirements.

You can download the full chapter free here. 

We’ve collaborated with our partners to make four limited edition versions of the book, including custom covers that highlight real-time ray tracing in Fortnite, Control, Watch Dogs: Legion, and Quake II RTX.

To win a limited edition print copy of Ray Tracing Gems II, enter the giveaway contest here: https://developer.nvidia.com/ray-tracing-gems-ii

Walk into a store. Grab your stuff. And walk right out again, without stopping to check out. In just the past three months, California-based AiFi has helped Choice Market increase sales at one of its Denver stores by 20 percent among customers who opted to skip the checkout line. It allowed Żappka, a Polish convenience Read article >

The post Shopping Smart: AiFi Using AI to Spark a Retail Renaissance appeared first on The Official NVIDIA Blog.

This post was updated July 20, 2021 to reflect NVIDIA TensorRT 8.0 updates. In this post, you learn how to deploy TensorFlow trained deep learning models using the new TensorFlow-ONNX-TensorRT workflow. This tutorial uses NVIDIA TensorRT 8.0.0.3 and provides two code samples, one for TensorFlow v1 and one for TensorFlow v2. TensorRT is an inference … Continued

This post was updated July 20, 2021 to reflect NVIDIA TensorRT 8.0 updates.

In this post, you learn how to deploy TensorFlow trained deep learning models using the new TensorFlow-ONNX-TensorRT workflow. This tutorial uses NVIDIA TensorRT 8.0.0.3 and provides two code samples, one for TensorFlow v1 and one for TensorFlow v2. TensorRT is an inference accelerator.

First, a network is trained using any framework. After a network is trained, the batch size and precision are fixed (with precision as FP32, FP16, or INT8). The trained model is passed to the TensorRT optimizer, which outputs an optimized runtime also called a plan. The .plan file is a serialized file format of the TensorRT engine. The plan file must be deserialized to run inference using the TensorRT runtime. 

To optimize models implemented in TensorFlow, the only thing you have to do is convert models to the ONNX format and use the ONNX parser in TensorRT to parse the model and build the TensorRT engine. Figure 1 shows the high-level ONNX workflow. 

In this post, we discuss how to create a TensorRT engine using the ONNX workflow and how to run inference from the TensorRT engine. More specifically, we demonstrate end-to-end inference from a model in Keras or TensorFlow to ONNX, and to the TensorRT engine with ResNet-50, semantic segmentation, and U-Net networks. Finally, we explain how you can use this workflow on other networks. 

Download the code examples and unzip. You can run either the TensorFlow 1 or the TensorFlow 2 code example by follow the appropriate README. After downloading the file, you should also download labels.py from the Cityscapes dataset scripts repo and place it in the same folder as the other scripts.

ONNX overview

ONNX is an open format for machine learning and deep learning models. It allows you to convert deep learning and machine learning models from different frameworks such as TensorFlow, PyTorch, MATLAB, Caffe, and Keras to a single format.

It defines a common set of operators, common sets of building blocks of deep learning, and a common file format. It provides a definition of a computation graph, as well as built-in operators. The list of ONNX nodes that may have one or more inputs or outputs forms an acyclic graph. 

ResNet ONNX workflow example

In this example, we show how to use the ONNX workflow on two different networks and create a TensorRT engine. The first network is ResNet-50.

The workflow consists of the following steps:

1. Convert the TensorFlow/Keras model to a .pb file.
2. Convert the .pb file to the ONNX format.
3. Create a TensorRT engine. 
4. Run inference from the TensorRT engine.

Requirements

#IF TensorFlow 1
pip install tensorflow-gpu==1.15.0 keras==2.3.1
#IF TensorFlow 2
pip install tensorflow-gpu==2.4.0 keras==2.4.3

#Other requirements
pip install -U keras2onnx tf2onnx==1.8.2 pillow pycuda scikit-image

#Check installation
python -c "import keras;print(keras.version)"

Converting the model to .pb

The first step is to convert the model to a .pb file. The following code example converts the ResNet-50 model to a .pb file:

import tensorflow as tf
import keras
from tensorflow.keras.models import Model
import keras.backend as K
K.set_learning_phase(0)

def keras_to_pb(model, output_filename, output_node_names):

   """
   This is the function to convert the Keras model to pb.

   Args:
      model: The Keras model.
      output_filename: The output .pb file name.
      output_node_names: The output nodes of the network. If None, then
      the function gets the last layer name as the output node.
   """

   # Get the names of the input and output nodes.
   in_name = model.layers[0].get_output_at(0).name.split(':')[0]

   if output_node_names is None:
       output_node_names = [model.layers[-1].get_output_at(0).name.split(':')[0]]

   sess = keras.backend.get_session()

   # The TensorFlow freeze_graph expects a comma-separated string of output node names.
   output_node_names_tf = ','.join(output_node_names)

   frozen_graph_def = tf.graph_util.convert_variables_to_constants(
       sess,
       sess.graph_def,
       output_node_names)

   sess.close()
   wkdir = ''
   tf.train.write_graph(frozen_graph_def, wkdir, output_filename, as_text=False)

   return in_name, output_node_names

# load the ResNet-50 model pretrained on imagenet
model = keras.applications.resnet.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

# Convert the Keras ResNet-50 model to a .pb file
in_tensor_name, out_tensor_names = keras_to_pb(model, "models/resnet50.pb", None) 

In addition to Keras, you can also download ResNet-50 from the following locations:

• Deep Learning Examples GitHub repository: Provides the latest deep learning example networks. You can also see the ResNet-50 branch, which contains a script and recipe to train the ResNet-50 v1.5 model. 
• NVIDIA NGC Models: It has the list of checkpoints for pretrained models. As an example, search on ResNet-50v1.5 for TensorFlow and get the latest checkpoint from the Download page.

Converting the .pb file to ONNX 

The second step is to convert the .pb model to the ONNX format. To do this, first install tf2onnx. 

After installing tf2onnx, there are two ways of converting the model from a .pb file to the ONNX format. The first way is to use the command line and the second method is by using Python API. Run the following command:

python -m tf2onnx.convert  --input /Path/to/resnet50.pb --inputs input_1:0 --outputs probs/Softmax:0 --output resnet50.onnx 

Creating the TensorRT engine from ONNX

To create the TensorRT engine from the ONNX file, run the following command: 

import tensorrt as trt

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(TRT_LOGGER)
def build_engine(onnx_path, shape = [1,224,224,3]):

   """
   This is the function to create the TensorRT engine
   Args:
      onnx_path : Path to onnx_file. 
      shape : Shape of the input of the ONNX file. 
  """
   with trt.Builder(TRT_LOGGER) as builder, builder.create_network(1) as network, builder.create_builder_config() as config, trt.OnnxParser(network, TRT_LOGGER) as parser:
       config.max_workspace_size = (256 with open(onnx_path, 'rb') as model:
           parser.parse(model.read())
       network.get_input(0).shape = shape
       engine = builder.build_engine(network, config)
       return engine

def save_engine(engine, file_name):
   buf = engine.serialize()
   with open(file_name, 'wb') as f:
       f.write(buf)
def load_engine(trt_runtime, plan_path):
   with open(plan_path, 'rb') as f:
       engine_data = f.read()
   engine = trt_runtime.deserialize_cuda_engine(engine_data)
   return engine

This code should be saved in the engine.py file, and is used later in the post.

This code example contains the following variable:  

• max_workspace_size:  Maximum GPU temporary memory that ICudaEngine can use at execution time.

The builder creates an empty network (builder.create_network()) and the ONNX parser parses the ONNX file into the network (parser.parse(model.read())). You set the input shape for the network (network.get_input(0).shape = shape), after which the builder creates the engine (engine = builder.build_cuda_engine(network)). To create the engine, run the following code example:

import engine as eng
import argparse
from onnx import ModelProto
import tensorrt as trt 
 
 engine_name = “resnet50.plan”
 onnx_path = "/path/to/onnx/result/file/"
 batch_size = 1 
 
 model = ModelProto()
 with open(onnx_path, "rb") as f:
    model.ParseFromString(f.read())
 
 d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
 d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
 d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
 shape = [batch_size , d0, d1 ,d2]
 engine = eng.build_engine(onnx_path, shape= shape)
 eng.save_engine(engine, engine_name) 

In this code example, you first get the input shape from the ONNX model. Next,  create the engine, and then save the engine in a .plan file.

Running inference from the TensorRT engine:

The TensorRT engine runs inference in the following workflow: 

1. Allocate buffers for inputs and outputs in the GPU.
2. Copy data from the host to the allocated input buffers in the GPU.
3. Run inference in the GPU. 
4. Copy results from the GPU to the host. 
5. Reshape the results as necessary. 

These steps are explained in detail in the following code example. This code should be saved in the inference.py file, and is used later in this post.

import tensorrt as trt
import pycuda.driver as cuda
import numpy as np
import pycuda.autoinit 

def allocate_buffers(engine, batch_size, data_type):

   """
   This is the function to allocate buffers for input and output in the device
   Args:
      engine : The path to the TensorRT engine. 
      batch_size : The batch size for execution time.
      data_type: The type of the data for input and output, for example trt.float32. 
   
   Output:
      h_input_1: Input in the host.
      d_input_1: Input in the device. 
      h_output_1: Output in the host. 
      d_output_1: Output in the device. 
      stream: CUDA stream.

   """

   # Determine dimensions and create page-locked memory buffers (which won't be swapped to disk) to hold host inputs/outputs.
   h_input_1 = cuda.pagelocked_empty(batch_size * trt.volume(engine.get_binding_shape(0)), dtype=trt.nptype(data_type))
   h_output = cuda.pagelocked_empty(batch_size * trt.volume(engine.get_binding_shape(1)), dtype=trt.nptype(data_type))
   # Allocate device memory for inputs and outputs.
   d_input_1 = cuda.mem_alloc(h_input_1.nbytes)

   d_output = cuda.mem_alloc(h_output.nbytes)
   # Create a stream in which to copy inputs/outputs and run inference.
   stream = cuda.Stream()
   return h_input_1, d_input_1, h_output, d_output, stream 

def load_images_to_buffer(pics, pagelocked_buffer):
   preprocessed = np.asarray(pics).ravel()
   np.copyto(pagelocked_buffer, preprocessed) 

def do_inference(engine, pics_1, h_input_1, d_input_1, h_output, d_output, stream, batch_size, height, width):
   """
   This is the function to run the inference
   Args:
      engine : Path to the TensorRT engine 
      pics_1 : Input images to the model.  
      h_input_1: Input in the host         
      d_input_1: Input in the device 
      h_output_1: Output in the host 
      d_output_1: Output in the device 
      stream: CUDA stream
      batch_size : Batch size for execution time
      height: Height of the output image
      width: Width of the output image
   
   Output:
      The list of output images

   """

   load_images_to_buffer(pics_1, h_input_1)

   with engine.create_execution_context() as context:
       # Transfer input data to the GPU.
       cuda.memcpy_htod_async(d_input_1, h_input_1, stream)

       # Run inference.

       context.profiler = trt.Profiler()
       context.execute(batch_size=1, bindings=[int(d_input_1), int(d_output)])

       # Transfer predictions back from the GPU.
       cuda.memcpy_dtoh_async(h_output, d_output, stream)
       # Synchronize the stream
       stream.synchronize()
       # Return the host output.
       out = h_output.reshape((batch_size,-1, height, width))
       return out 

The first two lines are for determining the dimensions for input and output. You create page-locked memory buffers in host (h_input_1, h_output). Then, you allocate device memory for input and output the same size as host input and output (d_input_1, d_output). The next step is to create the CUDA stream for copying data between the allocated memory from device and host. 

In this code example, in the do_inference function, the first step is to load images to buffers in the host using the load_images_to_buffer function. Then the input data is transferred to the GPU (cuda.memcpy_htod_async(d_input_1, h_input_1, stream)) and inference is run using context.execute. Finally the results are copied from GPU to the host (cuda.memcpy_dtoh_async(h_output, d_output, stream)).

Semantic segmentation ONNX workflow example

In the post Fast INT8 Inference for Autonomous Vehicles with TensorRT 3, the author covered the process of UFF workflow for a semantic segmentation model.

In this post, you use similar networks to run the ONNX workflow for semantic segmentation. The network consists of a VGG16-based encoder and three upsampling layers implemented using a deconvolutional layer. The network is trained in about 40,000 iterations on the Cityscapes Dataset. 

There are multiple ways of converting the TensorFlow model to an ONNX file. One way is the one explained in the ResNet50 section. Keras also has its own Keras-to-ONNX file converter. Sometimes, some of the layers are not supported in the TensorFlow-to-ONNX but they are supported in the Keras to ONNX converter. Depending on the Keras framework and the type of layers used, you may need to choose between converters. 

In the following code example, you directly convert the Keras model to ONNX using the Keras-to-ONNX converter. Download the pretrained semantic segmentation file, semantic_segmentation.hdf5.

import keras
import tensorflow as tf
from keras2onnx import convert_keras

def keras_to_onnx(model, output_filename):
   onnx = convert_keras(model, output_filename)
   with open(output_filename, "wb") as f:
       f.write(onnx.SerializeToString())

semantic_model = keras.models.load_model('/path/to/semantic_segmentation.hdf5')
keras_to_onnx(semantic_model, 'semantic_segmentation.onnx') 

Figure 2 shows the architecture of the network.

As in the previous example, use the following code example to create the engine for semantic segmentation.

import engine as eng
from onnx import ModelProto
import tensorrt as trt


engine_name = 'semantic.plan'
onnx_path = "semantic.onnx"
batch_size = 1

model = ModelProto()
with open(onnx_path, "rb") as f:
  model.ParseFromString(f.read())

d0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value
d1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value
d2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value
shape = [batch_size , d0, d1 ,d2]
engine = eng.build_engine(onnx_path, shape= shape)
eng.save_engine(engine, engine_name) 

To test the output of the model, use the Cityscapes Dataset. To work with Cityscapes, you must have the following functions: sub_mean_chw and color_map.  

In the following code example, sub_mean_chw is for subtracting the mean value from the image as the preprocessing step and color_map is the mapping from the class ID to a color. The latter is used for visualization. 

import numpy as np
from PIL import Image
import tensorrt as trt
import labels  # from cityscapes evaluation script
import skimage.transform

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(TRT_LOGGER)

MEAN = (71.60167789, 82.09696889, 72.30508881)
CLASSES = 20

HEIGHT = 512
WIDTH = 1024

def sub_mean_chw(data):
   data = data.transpose((1, 2, 0))  # CHW -> HWC
   data -= np.array(MEAN)  # Broadcast subtract
   data = data.transpose((2, 0, 1))  # HWC -> CHW
   return data

def rescale_image(image, output_shape, order=1):
   image = skimage.transform.resize(image, output_shape,
               order=order, preserve_range=True, mode='reflect')
   return image

def color_map(output):
   output = output.reshape(CLASSES, HEIGHT, WIDTH)
   out_col = np.zeros(shape=(HEIGHT, WIDTH), dtype=(np.uint8, 3))
   for x in range(WIDTH):
       for y in range(HEIGHT):

           if (np.argmax(output[:, y, x] )== 19):
               out_col[y,x] = (0, 0, 0)
           else:
               out_col[y, x] = labels.id2label[labels.trainId2label[np.argmax(output[:, y, x])].id].color
   return out_col  

The following code example is the rest of the code for the previous example. You must run the previous block first because you need the defined functions. Use the example to compare the output of the Keras model and TensorRT engine semantic .plan file and then visualize both outputs. Replace the placeholders /path/to/semantic_segmentation.hdf5  and input_file_path as appropriate.

import engine as eng
import inference as inf
import keras
import tensorrt as trt 

input_file_path = ‘munster_000172_000019_leftImg8bit.png’
onnx_file = "semantic.onnx"
serialized_plan_fp32 = "semantic.plan"
HEIGHT = 512
WIDTH = 1024

image = np.asarray(Image.open(input_file_path))
img = rescale_image(image, (512, 1024),order=1)
im = np.array(img, dtype=np.float32, order='C')
im = im.transpose((2, 0, 1))
im = sub_mean_chw(im)

engine = eng.load_engine(trt_runtime, serialized_plan_fp32)
h_input, d_input, h_output, d_output, stream = inf.allocate_buffers(engine, 1, trt.float32)
out = inf.do_inference(engine, im, h_input, d_input, h_output, d_output, stream, 1, HEIGHT, WIDTH)
out = color_map(out)

colorImage_trt = Image.fromarray(out.astype(np.uint8))
colorImage_trt.save(“trt_output.png”)

semantic_model = keras.models.load_model('/path/to/semantic_segmentation.hdf5')
out_keras= semantic_model.predict(im.reshape(-1, 3, HEIGHT, WIDTH))

out_keras = color_map(out_keras)
colorImage_k = Image.fromarray(out_keras.astype(np.uint8))
colorImage_k.save(“keras_output.png”)

Figure 3 shows the actual image and the ground truth, and the output of Keras versus the output of the TensorRT engine. As you can see, the output for the TensorRT engine is similar to the one for Keras. 

Try it on other networks

Now you can try the ONNX workflow on other networks. For more information about good examples of segmentation networks, see Segmentation models with pretrained backbones on GitHub. 

As an example, we show how to use the ONNX workflow with other networks. The network in this example is U-Net from the segmentation_models library. Here, we only loaded the model and did not train it. You may need to train these models on your preferred dataset. 

One important point about these networks is that when you load these networks, their input layer sizes are as follows: (None, None, None, 3). To create a TensorRT engine, you need an ONNX file with a known input size. Before you convert this model to ONNX, change the network by assigning the size to its input and then convert it to the ONNX format. 

As an example, load the U-Net network from this library (segmentation_models) and assign the size (244, 244, 3) to its input. After creating the TensorRT engine for the inference, do a similar conversion to what you did for semantic segmentation. Depending on the application and dataset, you may need to have a different color mapping.

# Requirement for TensorFlow 2
pip install tensorflow-gpu==2.1.0
 
# Other requirements
pip install -U segmentation-models

import segmentation_models as sm
import keras
from keras2onnx import convert_keras
from engine import *
onnx_path = 'unet.onnx'
engine_name = 'unet.plan'
batch_size = 1
CHANNEL = 3
HEIGHT = 224
WIDTH = 224

model = sm.Unet()
model._layers[0].batch_input_shape = (None, 224,224,3)
model = keras.models.clone_model(model)

onx = convert_keras(model, onnx_path)
with open(onnx_path, "wb") as f:
  f.write(onx.SerializeToString())

shape = [batch_size , HEIGHT, WIDTH, CHANNEL]
engine = build_engine(onnx_path, shape= shape)
save_engine(engine, engine_name) 

As we mentioned earlier in this post, another way of downloading pretrained models is to download them from NVIDIA NGC Models. It has a list of checkpoints for pretrained models. As an example, you can search for UNet for TensorFlow and then go to the Download page to get the latest checkpoint. 

Conclusion

In this post, we explained how to deploy deep learning applications using a TensorFlow-to-ONNX-to-TensorRT workflow, with several examples. The first example was ONNX-TensorRT on ResNet-50, and the second example was VGG16-based semantic segmentation that was trained on the Cityscapes Dataset. At the end of the post, we demonstrated how to apply this workflow on other networks.  For more information about the best performance of training and inference, see NVIDIA Data Center Deep Learning Product Performance.

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NVIDIA announced TensorRT 8.0 which brings BERT-Large inference latency down to 1.2 ms with new optimizations.

Today, NVIDIA announced TensorRT 8.0 which brings BERT-Large inference latency down to 1.2 ms with new optimizations. This version also delivers 2x the accuracy for INT8 precision with Quantization Aware Training, and significantly higher performance through support for Sparsity, which was introduced in Ampere GPUs.

TensorRT is an SDK for high-performance deep learning inference that includes an inference optimizer and runtime that delivers low latency and high throughput. TensorRT is used across industries such as Healthcare, Automotive, Manufacturing, Internet/Telecom services, Financial Services, Energy, and has been downloaded nearly 2.5 million times.

There have been several kinds of new transformer-based models used across conversational AI. New generalized optimizations in TensorRT can accelerate all such models reducing inference time to half the time vs TensorRT 7.

Highlights from this version include:

• BERT Inference in 1.2 ms with new transformer optimizations
• Achieve accuracy equivalent to FP32 with INT8 precision using Quantization Aware Training
• Introducing Sparsity support for faster inference on Ampere GPUs

You can learn more about Sparsity here.

One of the biggest social media platforms in China, WeChat accelerates its search using TensorRT serving 500M users a month.

“We have implemented TensorRT-and-INT8 QAT-based model inference acceleration to accelerate core tasks of WeChat Search such as Query Understanding and Results Ranking. The conventional limitation of NLP model complexity has been broken-through by our solution with GPU + TensorRT, and BERT/Transformer can be fully integrated in our solution. In addition, we have achieved significant reduction (70%) in allocated computational resources using superb performance optimization methods. ” – Huili/Raccoonliu/Dickzhu, WeChat Search

To learn more about TensorRT 8 and its features:

• Real-Time Natural Language Understanding with BERT Using TensorRT
• Achieving FP32 Accuracy for INT8 Inference using Quantization Aware Training with TensorRT
• Accelerating Inference with Sparsity using Ampere Architecture and TensorRT
• Speeding Up Deep Learning Inference Using TensorRT
• Importing models from TensorFlow and ONNX
• TensorRT Quick Start Guide
• Notebook: Optimize Object Detection with EfficientDet and TensorRT
• Notebook: BERT with QAT and Sparsity

Follow these GTC Sessions to get yourself familiar with Technologies:

• GTC Session S31876: Accelerate Deep Learning Inference with TensorRT 8.0
• GTC Session S31552: Making the Most of Structured Sparsity in the NVIDIA Ampere Architecture
• GTC Session S31653: Quantization Aware Training in PyTorch with TensorRT 8.0
• GTC Session S32224: Accelerating Deep Learning Inference with OnnxRuntime-TensorRT
• GTC Session S31732: Inference with Tensorflow 2 Integrated with TensorRT Session
• GTC Session S31828: TensorRT Quick Start Guide

NVIDIA TensorRT is freely available to members of the NVIDIA Developer Program. To learn more, visit TensorRT product page.

The final push for the hat trick came down to the wire. Five minutes before the deadline, the team submitted work in its third and hardest data science competition of the year in recommendation systems. Called RecSys, it’s a relatively new branch of computer science that’s spawned one of the most widely used applications in Read article >

The post Just What You’re Looking For: Recommender Team Suggests Winning Strategies appeared first on The Official NVIDIA Blog.

NVIDIA today launched TensorRT™ 8, the eighth generation of the company’s AI software, which slashes inference time in half for language queries — enabling developers to build the world’s best-performing search engines, ad recommendations and chatbots and offer them from the cloud to the edge.

○ TensorRT is an SDK for high-performance deep learning inference, and TensorRT 8.0 introduces support for sparsity that uses sparse tensor cores on NVIDIA Ampere GPUs. It can accelerate networks by reducing the computation of zeros present in GEMM operations in neural networks. You get a performance gain compared to dense networks by just following the steps in this post.

This post was updated July 20, 2021 to reflect NVIDIA TensorRT 8.0 updates.

When deploying a neural network, it’s useful to think about how the network could be made to run faster or take less space. A more efficient network can make better predictions in a limited time budget, react more quickly to unexpected input, or fit into constrained deployment environments.

Sparsity is one optimization technique that holds the promise of meeting these goals. If there are zeros in the network, then you don’t need to store or operate on them. The benefits of sparsity only seem straightforward. There have long been three challenges to realizing the promised gains.

• Acceleration—Fine-grained, unstructured, weight sparsity lacks structure and cannot use the vector and matrix instructions available in efficient hardware to accelerate common network operations. Standard sparse formats are inefficient for all but high sparsities.
• Accuracy—To achieve a useful speedup with fine-grained, unstructured sparsity, the network must be made sparse, which often causes accuracy loss. Alternate pruning methods that attempt to make acceleration easier, such as coarse-grained pruning that removes blocks of weights, channels, or entire layers, can run into accuracy trouble even sooner. This limits the potential performance benefit.
• Workflow—Much of the current research in network pruning serves as useful existence proofs. It has been shown that network A can achieve Sparsity X. The trouble comes when you try to apply Sparsity X to network B. It may not work due to differences in the network, task, optimizer, or any hyperparameter.

In this post, we discuss how the NVIDIA Ampere Architecture addresses these challenges. Today, NVIDIA is releasing TensorRT version 8.0, which introduces support for the Sparse Tensor Cores available on the NVIDIA Ampere Architecture GPUs.

TensorRT is an SDK for high-performance deep learning inference, which includes an optimizer and runtime that minimizes latency and maximizes throughput in production. Using a simple training workflow and deploying with TensorRT 8.0, Sparse Tensor Cores can eliminate unnecessary calculations in neural networks, resulting in over 30% performance/watt gain compared to dense networks.

Sparse Tensor Cores accelerate 2:4 fine-grained structured sparsity

The NVIDIA A100 GPU adds support for fine-grained structured sparsity to its Tensor Cores.  Sparse Tensor Cores accelerate a 2:4 sparsity pattern. In each contiguous block of four values, two values must be zero. This naturally leads to a sparsity of 50%, which is fine-grained. There are no vector or block structures pruned together. Such a regular pattern is easy to compress and has a low metadata overhead (Figure 1).

Sparse Tensor Cores accelerate this format by operating only on the nonzero values in the compressed matrix. They use the metadata that is stored with the nonzeros to pull only the necessary values from the other, uncompressed operand. So, for a sparsity of 2x, they can complete the same effective calculation in half the time. Table 1 shows details on the wide variety of data types supported by Sparse Tensor Cores.

2:4 structured sparse networks maintain accuracy

Of course, performance is pointless without good accuracy. We’ve developed a simple training workflow that can easily generate a 2:4 structured sparse network matching the accuracy of the dense network:

1. Start with a dense network. The goal is to start with a known-good model whose weights have converged to give useful results.
2. On the dense network, prune the weights to satisfy the 2:4 structured sparsity criteria. Out of every four elements, remove just two.
3. Repeat the original training procedure.

This workflow uses one-shot pruning in Step 2. After the pruning stage, the sparsity pattern is fixed. There are many ways to make pruning decisions. Which weights should stay, and which should be forced to zero? We’ve found that a simple answer works well: weight magnitude. We prefer to prune values that are already close to zero. 

As you might expect, suddenly turning half of the weights in a network to zero can affect the network’s accuracy. Step 3 recovers that accuracy with enough weight update steps to let the weights converge and a high enough learning rate to let the weights move around sufficiently.  This recipe works incredibly well. Across a wide range of networks, it generates a sparse model that maintains the accuracy of the dense network from Step 1. 

Table 2 has a sample of FP16 accuracy results that we obtained using this workflow implemented in the PyTorch Library Automatic SParsity (ASP). For more information about the full results for both FP16 and INT8, see the Accelerating Sparse Deep Neural Networks whitepaper.

Case study: ResNeXt-101_32x8d

Here’s how easy the workflow is to use with ResNeXt-101_32x8d as a target.

Generating the sparse model

You use the torchvision pretrained model, so step 1 is done already. Because you’re using ASP, the first code change is to import the library:

try:
    from apex.contrib.sparsity import ASP
except ImportError:
    raise RuntimeError("Failed to import ASP. Please install Apex from https:// github.com/nvidia/apex .")

Load the pretrained model for this training run. Instead of training the dense weights, though, prune the model and prepare the optimizer before the training loop (step 2 of the workflow):

ASP.prune_trained_model(model, optimizer)
print("Start training")
    start_time = time.time()
    for epoch in range(args.start_epoch, args.epochs):
    ...

That’s it. The training loop proceeds as normal with the default command augmented to begin with the pretrained model, which reuses the original hyperparameters and optimizer settings for the retraining:

python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py
    --model resnext101_32x8d --epochs 100 --pretrained True

When training completes (Step 3), the network accuracy should have recovered to match that of the pretrained model, as shown in Table 2. As usual, the best-performing checkpoint may not be from the final epoch.

Preparing for inference

For inference, use TensorRT 8.0 to import the trained model’s sparse checkpoint. The model needs to be converted from the native framework format into the ONNX format before importing into TensorRT. Conversion can be done by following the notebooks in the quickstart/IntroNotebooks GitHub repo.

We have already converted the sparse ResNeXt-101_32x8d to ONNX format. You can download this model from NGC. If you don’t have NGC installed, use the following command to install NGC:

cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && unzip ngccli_cat_linux.zip && chmod u+x ngc && rm ngccli_cat_linux.zip ngc.md5 && echo "no-apikeynasciin" | ngc config set

After NGC is installed, download sparse ResNeXt-101_32x8d in ONNX format by running the following command:

ngc registry model download-version nvidia/resnext101_32x8d_dense_onnx:1"

To import the ONNX model into TensorRT, clone the TensorRT repo and set up the Docker environment, as mentioned in the NVIDIA/TensorRT readme.

After you are in the TensorRT root directory, convert the sparse ONNX model to TensorRT engine using trtexec. Make a directory to store the model and engine:

cd /workspace/TensorRT/
mkdir model

Copy the downloaded ResNext ONNX model to the /workspace/TensorRT/model directory and then execute the trtexec command as follows:

./workspace/TensorRT/build/out/trtexec  --onnx=/workspace/TensorRT/model/resnext101_32x8d_sparse_fp32.onnx  --saveEngine=/workspace/TensorRT/model/resnext101_engine.trt  
--explicitBatch 
--sparsity=enable 
--fp16

A new file named resnext101_engine.trt is created at /workspace/TensorRT/model/. The resnext101_engine.trt file can now be serialized to perform inference by one of the following methods:

• TensorRT runtime in C++ or Python, as shown in this example notebook
• NVIDIA Triton Inference Server

Performance in TensorRT 8.0

Benchmarking this sparse model in TensorRT 8.0 on an A100 GPU at various batch sizes shows two important trends:

• Performance benefits increase with the amount of work that the A100 is doing. Larger batch sizes generally lead to larger improvements, approaching 20% at the high end.
• At smaller batch sizes, where the A100 clock speeds can stay low, using sparsity allows them to be pushed even lower for the same performance, which results in power efficiency improvements greater than the performance itself, leading to up to a 36% performance/watt gain.

Don’t forget, this network has the exact same accuracy as the dense baseline. This extra efficiency and performance doesn’t require penalizing accuracy.

Summary

Sparsity is popular in neural network compression and simplification research. Until now, though, fine-grained sparsity has not delivered on its promise of performance andaccuracy. We developed 2:4 fine-grained structured sparsity and built support directly into NVIDIA Ampere Architecture Sparse Tensor Cores. With this simple, three-step sparse retraining workflow, you can generate sparse neural networks that match the baseline accuracy, and TensorRT 8.0 accelerates them by default.

For more information, see the Making the Most of Structured Sparsity in the NVIDIA Ampere Architecture GTC2021 session, all about accelerating sparsity in the NVIDIA Ampere Architecture, or read the Accelerating Sparse Deep Neural Networks whitepaper.

Ready to jump in and try 2:4 sparsity on your own networks? The Automatic SParsity (ASP) PyTorch library makes it easy to generate a sparse network, and TensorRT 8.0 can deploy them efficiently.

To learn more about TensorRT 8.0 and it’s new features, see the Accelerate Deep Learning Inference with TensorRT 8.0 GTC’21 session or the TensorRT page.